PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Stochastic text models for music categorization
Carlos Pérez-Sancho, David Rizo and José Iñesta
Lecture Notes in Computer Science Volume 5342, pp. 55-64, 2008. ISSN 0302-9743


Music genre meta-data is of paramount importance for the organization of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research. This work brings to symbolic music recognition some technologies, like the stochastic language models, already successfully applied to text categorization. In this work we model chord progressions and melodies as n-grams and strings and then apply perplexity and naïve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Also a combination of the different techniques as an ensemble of classifiers is proposed. Some genres and sub-genres among popular, jazz, and academic music have been considered. The results show that the ensemble is a good trade-off approach able to perform well without the risk of choosing the wrong classifier.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Information Retrieval & Textual Information Access
ID Code:5167
Deposited By:Carlos Pérez-Sancho
Deposited On:24 March 2009